Overview

Dataset statistics

Number of variables24
Number of observations96298
Missing cells0
Missing cells (%)0.0%
Duplicate rows451
Duplicate rows (%)0.5%
Total size in memory18.4 MiB
Average record size in memory200.0 B

Variable types

Numeric21
Categorical3

Warnings

Dataset has 451 (0.5%) duplicate rowsDuplicates
SEQUENCE_NO is highly correlated with Y2High correlation
ENTRY is highly correlated with REVENUE and 3 other fieldsHigh correlation
REVENUE is highly correlated with ENTRY and 3 other fieldsHigh correlation
WINNINGS_1 is highly correlated with ENTRY and 2 other fieldsHigh correlation
DISCOUNT is highly correlated with ENTRY and 3 other fieldsHigh correlation
DEPOSIT is highly correlated with ENTRY and 4 other fieldsHigh correlation
DEPOSIT_2 is highly correlated with DISCOUNT and 1 other fieldsHigh correlation
WITHDRAW is highly correlated with WITHDRAW_NUMBERHigh correlation
WITHDRAW_NUMBER is highly correlated with WITHDRAWHigh correlation
ENTRY_NUMBER is highly correlated with WINNINGS_NUMBERHigh correlation
WINNINGS_NUMBER is highly correlated with ENTRY_NUMBERHigh correlation
PRACTICE_ENTRY is highly correlated with PRACTICE_WINNINGS and 2 other fieldsHigh correlation
PRACTICE_WINNINGS is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
PRACTICE_ENTRY_NUMBER is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
PRACTICE_WINNINGS_NUMBER is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
Y2 is highly correlated with SEQUENCE_NOHigh correlation
SEQUENCE_NO is highly correlated with WINNINGS_2 and 3 other fieldsHigh correlation
ENTRY is highly correlated with REVENUE and 6 other fieldsHigh correlation
REVENUE is highly correlated with ENTRY and 6 other fieldsHigh correlation
WINNINGS_1 is highly correlated with ENTRY and 5 other fieldsHigh correlation
WINNINGS_2 is highly correlated with SEQUENCE_NOHigh correlation
DISCOUNT is highly correlated with ENTRY and 2 other fieldsHigh correlation
DEPOSIT is highly correlated with ENTRY and 3 other fieldsHigh correlation
DEPOSIT_NUMBER is highly correlated with DEPOSITHigh correlation
DEPOSIT_2 is highly correlated with SEQUENCE_NO and 2 other fieldsHigh correlation
WITHDRAW is highly correlated with WITHDRAW_NUMBERHigh correlation
WITHDRAW_NUMBER is highly correlated with WITHDRAWHigh correlation
ENTRY_NUMBER is highly correlated with ENTRY and 3 other fieldsHigh correlation
WINNINGS_NUMBER is highly correlated with ENTRY and 3 other fieldsHigh correlation
PRACTICE_ENTRY is highly correlated with PRACTICE_WINNINGS and 2 other fieldsHigh correlation
PRACTICE_WINNINGS is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
PRACTICE_ENTRY_NUMBER is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
PRACTICE_WINNINGS_NUMBER is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
Y1 is highly correlated with SEQUENCE_NO and 4 other fieldsHigh correlation
Y2 is highly correlated with SEQUENCE_NO and 2 other fieldsHigh correlation
SEQUENCE_NO is highly correlated with DEPOSIT_2 and 1 other fieldsHigh correlation
ENTRY is highly correlated with REVENUE and 2 other fieldsHigh correlation
REVENUE is highly correlated with ENTRY and 2 other fieldsHigh correlation
WINNINGS_1 is highly correlated with ENTRY and 1 other fieldsHigh correlation
DEPOSIT is highly correlated with ENTRY and 2 other fieldsHigh correlation
DEPOSIT_NUMBER is highly correlated with DEPOSITHigh correlation
DEPOSIT_2 is highly correlated with SEQUENCE_NOHigh correlation
WITHDRAW is highly correlated with WITHDRAW_NUMBERHigh correlation
WITHDRAW_NUMBER is highly correlated with WITHDRAWHigh correlation
ENTRY_NUMBER is highly correlated with WINNINGS_NUMBERHigh correlation
WINNINGS_NUMBER is highly correlated with ENTRY_NUMBERHigh correlation
PRACTICE_ENTRY is highly correlated with PRACTICE_WINNINGS and 2 other fieldsHigh correlation
PRACTICE_WINNINGS is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
PRACTICE_ENTRY_NUMBER is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
PRACTICE_WINNINGS_NUMBER is highly correlated with PRACTICE_ENTRY and 2 other fieldsHigh correlation
Y1 is highly correlated with Y2High correlation
Y2 is highly correlated with SEQUENCE_NO and 1 other fieldsHigh correlation
DEPOSIT is highly correlated with Y1 and 3 other fieldsHigh correlation
PRACTICE_ENTRY_NUMBER is highly correlated with PRACTICE_WINNINGS_NUMBER and 2 other fieldsHigh correlation
Y1 is highly correlated with DEPOSIT and 1 other fieldsHigh correlation
DISCOUNT is highly correlated with DEPOSIT_2High correlation
PRACTICE_WINNINGS_NUMBER is highly correlated with PRACTICE_ENTRY_NUMBER and 1 other fieldsHigh correlation
PRACTICE_ENTRY is highly correlated with PRACTICE_ENTRY_NUMBER and 2 other fieldsHigh correlation
WINNINGS_1 is highly correlated with REVENUE and 2 other fieldsHigh correlation
REVENUE is highly correlated with DEPOSIT and 4 other fieldsHigh correlation
DEPOSIT_2 is highly correlated with DEPOSIT and 1 other fieldsHigh correlation
STATUS_CHECK is highly correlated with Y2 and 1 other fieldsHigh correlation
WINNINGS_NUMBER is highly correlated with ENTRY_NUMBERHigh correlation
ENTRY is highly correlated with WINNINGS_1 and 1 other fieldsHigh correlation
Y2 is highly correlated with STATUS_CHECK and 1 other fieldsHigh correlation
WITHDRAW is highly correlated with DEPOSIT and 1 other fieldsHigh correlation
WITHDRAW_NUMBER is highly correlated with WITHDRAWHigh correlation
SEQUENCE_NO is highly correlated with STATUS_CHECK and 1 other fieldsHigh correlation
PRACTICE_WINNINGS is highly correlated with PRACTICE_ENTRY_NUMBER and 1 other fieldsHigh correlation
ENTRY_NUMBER is highly correlated with WINNINGS_1 and 2 other fieldsHigh correlation
ENTRY is highly skewed (γ1 = 20.84096938) Skewed
WINNINGS_1 is highly skewed (γ1 = 21.18369333) Skewed
WINNINGS_2 is highly skewed (γ1 = 38.97154544) Skewed
WITHDRAW is highly skewed (γ1 = 22.97651183) Skewed
PRACTICE_ENTRY is highly skewed (γ1 = 25.20468873) Skewed
PRACTICE_WINNINGS is highly skewed (γ1 = 28.30539502) Skewed
PRACTICE_WINNINGS_NUMBER is highly skewed (γ1 = 20.13494762) Skewed
Y1 is highly skewed (γ1 = 23.77190203) Skewed
ENTRY has 3166 (3.3%) zeros Zeros
REVENUE has 3205 (3.3%) zeros Zeros
WINNINGS_1 has 9585 (10.0%) zeros Zeros
WINNINGS_2 has 73109 (75.9%) zeros Zeros
DISCOUNT has 24642 (25.6%) zeros Zeros
WITHDRAW has 87477 (90.8%) zeros Zeros
WITHDRAW_NUMBER has 87477 (90.8%) zeros Zeros
DEPOSIT_TRAILS has 32090 (33.3%) zeros Zeros
ENTRY_NUMBER has 3127 (3.2%) zeros Zeros
WINNINGS_NUMBER has 9585 (10.0%) zeros Zeros
PRACTICE_ENTRY has 86672 (90.0%) zeros Zeros
PRACTICE_WINNINGS has 88830 (92.2%) zeros Zeros
PRACTICE_ENTRY_NUMBER has 86672 (90.0%) zeros Zeros
PRACTICE_WINNINGS_NUMBER has 88830 (92.2%) zeros Zeros
Y2 has 2618 (2.7%) zeros Zeros

Reproduction

Analysis started2021-09-03 14:04:04.339916
Analysis finished2021-09-03 14:06:09.636274
Duration2 minutes and 5.3 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

SEQUENCE_NO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.973841617
Minimum1
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:09.794780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5.5
Q312
95-th percentile15.5
Maximum15.5
Range14.5
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.13986149
Coefficient of variation (CV)0.7370201063
Kurtosis-1.345103422
Mean6.973841617
Median Absolute Deviation (MAD)4
Skewness0.4135264844
Sum671567
Variance26.41817613
MonotonicityNot monotonic
2021-09-03T14:06:10.059754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
19687
 
10.1%
1.59282
 
9.6%
15.57667
 
8.0%
26404
 
6.7%
2.55170
 
5.4%
34355
 
4.5%
3.53677
 
3.8%
153677
 
3.8%
43128
 
3.2%
14.52906
 
3.0%
Other values (20)40345
41.9%
ValueCountFrequency (%)
19687
10.1%
1.59282
9.6%
26404
6.7%
2.55170
5.4%
34355
4.5%
3.53677
 
3.8%
43128
 
3.2%
4.52843
 
3.0%
52508
 
2.6%
5.52258
 
2.3%
ValueCountFrequency (%)
15.57667
8.0%
153677
3.8%
14.52906
 
3.0%
142472
 
2.6%
13.52267
 
2.4%
131934
 
2.0%
12.51938
 
2.0%
121885
 
2.0%
11.51761
 
1.8%
111797
 
1.9%

STATUS_CHECK
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
69369 
1
26929 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96298
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
069369
72.0%
126929
 
28.0%

Length

2021-09-03T14:06:10.550567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T14:06:10.667088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
069369
72.0%
126929
 
28.0%

Most occurring characters

ValueCountFrequency (%)
069369
72.0%
126929
 
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number96298
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
069369
72.0%
126929
 
28.0%

Most occurring scripts

ValueCountFrequency (%)
Common96298
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
069369
72.0%
126929
 
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII96298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
069369
72.0%
126929
 
28.0%

CATEGORY_1
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
B
50384 
K
22972 
C
7623 
A
6890 
M
6125 
Other values (5)
 
2304

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96298
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
B50384
52.3%
K22972
23.9%
C7623
 
7.9%
A6890
 
7.2%
M6125
 
6.4%
G1370
 
1.4%
J535
 
0.6%
F137
 
0.1%
I132
 
0.1%
L130
 
0.1%

Length

2021-09-03T14:06:10.990551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T14:06:11.140473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
b50384
52.3%
k22972
23.9%
c7623
 
7.9%
a6890
 
7.2%
m6125
 
6.4%
g1370
 
1.4%
j535
 
0.6%
f137
 
0.1%
i132
 
0.1%
l130
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B50384
52.3%
K22972
23.9%
C7623
 
7.9%
A6890
 
7.2%
M6125
 
6.4%
G1370
 
1.4%
J535
 
0.6%
F137
 
0.1%
I132
 
0.1%
L130
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter96298
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B50384
52.3%
K22972
23.9%
C7623
 
7.9%
A6890
 
7.2%
M6125
 
6.4%
G1370
 
1.4%
J535
 
0.6%
F137
 
0.1%
I132
 
0.1%
L130
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin96298
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B50384
52.3%
K22972
23.9%
C7623
 
7.9%
A6890
 
7.2%
M6125
 
6.4%
G1370
 
1.4%
J535
 
0.6%
F137
 
0.1%
I132
 
0.1%
L130
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B50384
52.3%
K22972
23.9%
C7623
 
7.9%
A6890
 
7.2%
M6125
 
6.4%
G1370
 
1.4%
J535
 
0.6%
F137
 
0.1%
I132
 
0.1%
L130
 
0.1%

CATEGORY_2
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
I
52281 
D
18367 
A
10289 
B
10204 
F
 
1671
Other values (4)
 
3486

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters96298
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowI
3rd rowD
4th rowE
5th rowI

Common Values

ValueCountFrequency (%)
I52281
54.3%
D18367
 
19.1%
A10289
 
10.7%
B10204
 
10.6%
F1671
 
1.7%
C1527
 
1.6%
E750
 
0.8%
H715
 
0.7%
G494
 
0.5%

Length

2021-09-03T14:06:11.511106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T14:06:11.652361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
i52281
54.3%
d18367
 
19.1%
a10289
 
10.7%
b10204
 
10.6%
f1671
 
1.7%
c1527
 
1.6%
e750
 
0.8%
h715
 
0.7%
g494
 
0.5%

Most occurring characters

ValueCountFrequency (%)
I52281
54.3%
D18367
 
19.1%
A10289
 
10.7%
B10204
 
10.6%
F1671
 
1.7%
C1527
 
1.6%
E750
 
0.8%
H715
 
0.7%
G494
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter96298
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I52281
54.3%
D18367
 
19.1%
A10289
 
10.7%
B10204
 
10.6%
F1671
 
1.7%
C1527
 
1.6%
E750
 
0.8%
H715
 
0.7%
G494
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin96298
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I52281
54.3%
D18367
 
19.1%
A10289
 
10.7%
B10204
 
10.6%
F1671
 
1.7%
C1527
 
1.6%
E750
 
0.8%
H715
 
0.7%
G494
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII96298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I52281
54.3%
D18367
 
19.1%
A10289
 
10.7%
B10204
 
10.6%
F1671
 
1.7%
C1527
 
1.6%
E750
 
0.8%
H715
 
0.7%
G494
 
0.5%

ACTIVE_YN
Real number (ℝ≥0)

Distinct181
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9827583229
Minimum0
Maximum1
Zeros55
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:11.866020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8888888889
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06864449807
Coefficient of variation (CV)0.06984880867
Kurtosis53.95337064
Mean0.9827583229
Median Absolute Deviation (MAD)0
Skewness-6.388903264
Sum94637.66098
Variance0.004712067115
MonotonicityNot monotonic
2021-09-03T14:06:12.123954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184871
88.1%
0.5567
 
0.6%
0.6666666667548
 
0.6%
0.75546
 
0.6%
0.8533
 
0.6%
0.8333333333531
 
0.6%
0.8571428571442
 
0.5%
0.8888888889404
 
0.4%
0.875400
 
0.4%
0.9372
 
0.4%
Other values (171)7084
 
7.4%
ValueCountFrequency (%)
055
0.1%
0.038461538461
 
< 0.1%
0.071428571431
 
< 0.1%
0.083333333331
 
< 0.1%
0.10526315791
 
< 0.1%
0.11538461541
 
< 0.1%
0.13636363641
 
< 0.1%
0.13793103451
 
< 0.1%
0.15384615381
 
< 0.1%
0.161
 
< 0.1%
ValueCountFrequency (%)
184871
88.1%
0.9666666667357
 
0.4%
0.9655172414300
 
0.3%
0.9642857143318
 
0.3%
0.962962963296
 
0.3%
0.9615384615296
 
0.3%
0.96243
 
0.3%
0.9583333333255
 
0.3%
0.9565217391264
 
0.3%
0.9545454545269
 
0.3%

ENTRY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct79510
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.486250127
Minimum0
Maximum1490.136556
Zeros3166
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:12.380289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03571428571
Q10.2728682165
median0.7832493056
Q32.530480278
95-th percentile18.74637181
Maximum1490.136556
Range1490.136556
Interquartile range (IQR)2.257612061

Descriptive statistics

Standard deviation17.58659643
Coefficient of variation (CV)3.920110545
Kurtosis998.4824732
Mean4.486250127
Median Absolute Deviation (MAD)0.6428825917
Skewness20.84096938
Sum432016.9148
Variance309.2883741
MonotonicityNot monotonic
2021-09-03T14:06:12.608227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03166
 
3.3%
0.251073
 
1.1%
0.125605
 
0.6%
0.5531
 
0.6%
1276
 
0.3%
0.375187
 
0.2%
0.08333333333186
 
0.2%
0.75137
 
0.1%
0.0625132
 
0.1%
0.1666666667121
 
0.1%
Other values (79500)89884
93.3%
ValueCountFrequency (%)
03166
3.3%
7.5 × 10-51
 
< 0.1%
0.000121
 
< 0.1%
0.00041666666672
 
< 0.1%
0.000552
 
< 0.1%
0.0005833351
 
< 0.1%
0.00058571428571
 
< 0.1%
0.00058823529411
 
< 0.1%
0.00076093751
 
< 0.1%
0.0007751
 
< 0.1%
ValueCountFrequency (%)
1490.1365561
< 0.1%
1117.4294581
< 0.1%
980.73725831
< 0.1%
715.36251
< 0.1%
583.9401251
< 0.1%
583.251
< 0.1%
571.8451751
< 0.1%
5701
< 0.1%
563.89473331
< 0.1%
561.42274071
< 0.1%

REVENUE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct82954
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5019055
Minimum0
Maximum119.3497778
Zeros3205
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:12.848792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00375
Q10.03599385458
median0.1048052355
Q30.3287343676
95-th percentile2.107532785
Maximum119.3497778
Range119.3497778
Interquartile range (IQR)0.292740513

Descriptive statistics

Standard deviation1.726351421
Coefficient of variation (CV)3.439594547
Kurtosis677.5773057
Mean0.5019055
Median Absolute Deviation (MAD)0.086442541
Skewness17.35518308
Sum48332.49584
Variance2.980289228
MonotonicityNot monotonic
2021-09-03T14:06:13.102710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03205
 
3.3%
0.04767
 
0.8%
0.02403
 
0.4%
0.075230
 
0.2%
0.15198
 
0.2%
0.08191
 
0.2%
0.0375171
 
0.2%
0.01875161
 
0.2%
0.0075154
 
0.2%
0.00375125
 
0.1%
Other values (82944)90693
94.2%
ValueCountFrequency (%)
03205
3.3%
8.333333333 × 10-61
 
< 0.1%
1.75 × 10-51
 
< 0.1%
2.5 × 10-52
 
< 0.1%
2.916666667 × 10-51
 
< 0.1%
3.529411765 × 10-51
 
< 0.1%
3.764705882 × 10-51
 
< 0.1%
4.642857143 × 10-51
 
< 0.1%
5 × 10-51
 
< 0.1%
5.625 × 10-51
 
< 0.1%
ValueCountFrequency (%)
119.34977781
< 0.1%
110.76335831
< 0.1%
85.9051
< 0.1%
78.576396011
< 0.1%
70.169166661
< 0.1%
68.41
< 0.1%
50.94648751
< 0.1%
49.230974991
< 0.1%
48.171208341
< 0.1%
47.399256191
< 0.1%

WINNINGS_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct77024
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.344695599
Minimum0
Maximum1115.266667
Zeros9585
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:13.349922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1068535714
median0.4685094231
Q31.720490625
95-th percentile13.8535585
Maximum1115.266667
Range1115.266667
Interquartile range (IQR)1.613637054

Descriptive statistics

Standard deviation14.34229054
Coefficient of variation (CV)4.288070503
Kurtosis963.6838297
Mean3.344695599
Median Absolute Deviation (MAD)0.4481594231
Skewness21.18369333
Sum322087.4968
Variance205.7012979
MonotonicityNot monotonic
2021-09-03T14:06:13.577567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09585
 
10.0%
0.0425417
 
0.4%
0.085289
 
0.3%
0.02125252
 
0.3%
0.0022199
 
0.2%
0.1275160
 
0.2%
0.0044132
 
0.1%
0.01416666667122
 
0.1%
0.06375118
 
0.1%
0.315115
 
0.1%
Other values (77014)84909
88.2%
ValueCountFrequency (%)
09585
10.0%
7.586206897 × 10-51
 
< 0.1%
7.708333333 × 10-51
 
< 0.1%
9.5 × 10-51
 
< 0.1%
0.00012
 
< 0.1%
0.0001156251
 
< 0.1%
0.00012222222221
 
< 0.1%
0.00012619047621
 
< 0.1%
0.00013751
 
< 0.1%
0.000152
 
< 0.1%
ValueCountFrequency (%)
1115.2666671
< 0.1%
989.595551
< 0.1%
815.16854171
< 0.1%
698.311
< 0.1%
545.600051
< 0.1%
539.456651
< 0.1%
475.008751
< 0.1%
469.2950251
< 0.1%
466.3970371
< 0.1%
426.936891
< 0.1%

WINNINGS_2
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct12221
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02398006378
Minimum0
Maximum17.87674167
Zeros73109
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:13.795585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1039450517
Maximum17.87674167
Range17.87674167
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1699337657
Coefficient of variation (CV)7.086460119
Kurtosis2540.298662
Mean0.02398006378
Median Absolute Deviation (MAD)0
Skewness38.97154544
Sum2309.232182
Variance0.02887748471
MonotonicityNot monotonic
2021-09-03T14:06:14.019330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073109
75.9%
0.025146
 
0.2%
0.01666666667134
 
0.1%
0.008333333333127
 
0.1%
0.0625115
 
0.1%
0.04166666667108
 
0.1%
0.0125106
 
0.1%
0.005106
 
0.1%
0.02083333333106
 
0.1%
0.0178571428695
 
0.1%
Other values (12211)22146
 
23.0%
ValueCountFrequency (%)
073109
75.9%
6.166666667 × 10-56
 
< 0.1%
6.379310345 × 10-54
 
< 0.1%
6.607142857 × 10-57
 
< 0.1%
6.851851852 × 10-57
 
< 0.1%
7.115384615 × 10-54
 
< 0.1%
7.4 × 10-55
 
< 0.1%
7.708333333 × 10-55
 
< 0.1%
8.043478261 × 10-54
 
< 0.1%
8.409090909 × 10-51
 
< 0.1%
ValueCountFrequency (%)
17.876741671
< 0.1%
12.092961
< 0.1%
11.64151
< 0.1%
10.603723331
< 0.1%
10.019166671
< 0.1%
8.3983333331
< 0.1%
8.33548751
< 0.1%
8.1681251
< 0.1%
7.4730033331
< 0.1%
7.3360351851
< 0.1%

DISCOUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19664
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6456299781
Minimum0
Maximum58.3
Zeros24642
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:14.313258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.147826087
Q30.4611777778
95-th percentile2.894677778
Maximum58.3
Range58.3
Interquartile range (IQR)0.4611777778

Descriptive statistics

Standard deviation1.968071916
Coefficient of variation (CV)3.048296986
Kurtosis117.0927077
Mean0.6456299781
Median Absolute Deviation (MAD)0.147826087
Skewness8.743175515
Sum62172.87563
Variance3.873307068
MonotonicityNot monotonic
2021-09-03T14:06:14.609575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024642
 
25.6%
0.51952
 
2.0%
11708
 
1.8%
0.25900
 
0.9%
0.3333333333860
 
0.9%
0.2634
 
0.7%
0.1666666667548
 
0.6%
0.2489
 
0.5%
0.2464
 
0.5%
0.1437
 
0.5%
Other values (19654)63664
66.1%
ValueCountFrequency (%)
024642
25.6%
1 × 10-51
 
< 0.1%
2.5 × 10-51
 
< 0.1%
9.333333333 × 10-51
 
< 0.1%
9.411764706 × 10-51
 
< 0.1%
0.00010344827591
 
< 0.1%
0.00010714285711
 
< 0.1%
0.00016666666671
 
< 0.1%
0.00023
 
< 0.1%
0.00020689655171
 
< 0.1%
ValueCountFrequency (%)
58.31
< 0.1%
54.91
< 0.1%
491
< 0.1%
48.41
< 0.1%
47.21
< 0.1%
46.31
< 0.1%
45.62
< 0.1%
43.81
< 0.1%
411
< 0.1%
40.61
< 0.1%

DEPOSIT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6805
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8393325137
Minimum0
Maximum75
Zeros37
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:14.928127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01785714286
Q10.08333333333
median0.25
Q30.5588235294
95-th percentile3.5
Maximum75
Range75
Interquartile range (IQR)0.4754901961

Descriptive statistics

Standard deviation2.320383309
Coefficient of variation (CV)2.764557873
Kurtosis97.68141904
Mean0.8393325137
Median Absolute Deviation (MAD)0.1875
Skewness7.813951935
Sum80826.0424
Variance5.3841787
MonotonicityNot monotonic
2021-09-03T14:06:15.235171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258594
 
8.9%
0.1255960
 
6.2%
0.53913
 
4.1%
0.083333333333507
 
3.6%
0.06252347
 
2.4%
12048
 
2.1%
0.16666666671809
 
1.9%
0.051759
 
1.8%
0.041666666671363
 
1.4%
0.33333333331328
 
1.4%
Other values (6795)63670
66.1%
ValueCountFrequency (%)
037
 
< 0.1%
0.008333333333761
0.8%
0.008620689655370
0.4%
0.008928571429291
 
0.3%
0.0091071428571
 
< 0.1%
0.009259259259236
 
0.2%
0.009615384615230
 
0.2%
0.01195
 
0.2%
0.010166666671
 
< 0.1%
0.01041666667187
 
0.2%
ValueCountFrequency (%)
752
 
< 0.1%
65.867666671
 
< 0.1%
502
 
< 0.1%
505
< 0.1%
42.307692311
 
< 0.1%
41.416666671
 
< 0.1%
401
 
< 0.1%
38.51
 
< 0.1%
37.666666671
 
< 0.1%
37.52
 
< 0.1%

DEPOSIT_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1918
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04935366097
Minimum0
Maximum0.9
Zeros37
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:15.534149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004545454545
Q10.01666666667
median0.03333333333
Q30.06666666667
95-th percentile0.1266666667
Maximum0.9
Range0.9
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.04799394402
Coefficient of variation (CV)0.972449522
Kurtosis16.76146864
Mean0.04935366097
Median Absolute Deviation (MAD)0.02
Skewness2.779018716
Sum4752.658844
Variance0.002303418662
MonotonicityNot monotonic
2021-09-03T14:06:15.814265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.110775
 
11.2%
0.058944
 
9.3%
0.033333333336194
 
6.4%
0.0254132
 
4.3%
0.023464
 
3.6%
0.016666666672708
 
2.8%
0.014285714291967
 
2.0%
0.066666666671537
 
1.6%
0.01251436
 
1.5%
0.0033333333331350
 
1.4%
Other values (1908)53791
55.9%
ValueCountFrequency (%)
037
 
< 0.1%
0.0033333333331350
1.4%
0.003448275862692
0.7%
0.003571428571509
 
0.5%
0.003703703704433
 
0.4%
0.003846153846422
 
0.4%
0.004368
 
0.4%
0.004166666667343
 
0.4%
0.004347826087331
 
0.3%
0.004545454545346
 
0.4%
ValueCountFrequency (%)
0.92
 
< 0.1%
0.86666666671
 
< 0.1%
0.851
 
< 0.1%
0.751
 
< 0.1%
0.652
 
< 0.1%
0.651
 
< 0.1%
0.61666666671
 
< 0.1%
0.69
< 0.1%
0.57222222221
 
< 0.1%
0.556
< 0.1%

DEPOSIT_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct603
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2986342721
Minimum0
Maximum25
Zeros42
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:16.089431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008928571429
Q10.02272727273
median0.0625
Q30.2272727273
95-th percentile1
Maximum25
Range25
Interquartile range (IQR)0.2045454545

Descriptive statistics

Standard deviation1.058917248
Coefficient of variation (CV)3.545866455
Kurtosis195.4243226
Mean0.2986342721
Median Absolute Deviation (MAD)0.04861111111
Skewness11.50002826
Sum28757.88313
Variance1.121305738
MonotonicityNot monotonic
2021-09-03T14:06:16.385186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258165
 
8.5%
0.1256565
 
6.8%
0.083333333334644
 
4.8%
0.53568
 
3.7%
0.06253413
 
3.5%
0.0083333333332942
 
3.1%
0.052779
 
2.9%
0.016666666672639
 
2.7%
0.035714285712479
 
2.6%
0.033333333332420
 
2.5%
Other values (593)56684
58.9%
ValueCountFrequency (%)
042
 
< 0.1%
0.0083333333332942
3.1%
0.00851
 
< 0.1%
0.0086206896551362
1.4%
0.0089285714291099
 
1.1%
0.0089655172411
 
< 0.1%
0.0091071428571
 
< 0.1%
0.009259259259858
 
0.9%
0.009615384615865
 
0.9%
0.0098214285712
 
< 0.1%
ValueCountFrequency (%)
2548
 
< 0.1%
209
 
< 0.1%
17.51
 
< 0.1%
171
 
< 0.1%
157
 
< 0.1%
12.538
 
< 0.1%
12.51
 
< 0.1%
11.254
 
< 0.1%
1017
 
< 0.1%
10273
0.3%

WITHDRAW
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2955
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04601148219
Minimum0
Maximum31.54136
Zeros87477
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:16.673347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1
Maximum31.54136
Range31.54136
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3742733213
Coefficient of variation (CV)8.134346113
Kurtosis999.9790806
Mean0.04601148219
Median Absolute Deviation (MAD)0
Skewness22.97651183
Sum4430.813712
Variance0.140080519
MonotonicityNot monotonic
2021-09-03T14:06:16.947089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
087477
90.8%
0.01333333333245
 
0.3%
0.03333333333170
 
0.2%
0.06666666667162
 
0.2%
0.01379310345100
 
0.1%
0.0266666666784
 
0.1%
0.133333333381
 
0.1%
0.0480
 
0.1%
0.277
 
0.1%
0.0466666666776
 
0.1%
Other values (2945)7746
 
8.0%
ValueCountFrequency (%)
087477
90.8%
0.0068472727271
 
< 0.1%
0.010286666671
 
< 0.1%
0.01333333333245
 
0.3%
0.01341
 
< 0.1%
0.013666666672
 
< 0.1%
0.01379310345100
 
0.1%
0.013862068971
 
< 0.1%
0.013931034481
 
< 0.1%
0.0143
 
< 0.1%
ValueCountFrequency (%)
31.541361
< 0.1%
20.755555561
< 0.1%
17.171106671
< 0.1%
16.041333331
< 0.1%
15.337773331
< 0.1%
14.9983481
< 0.1%
13.9541
< 0.1%
13.354666671
< 0.1%
13.061
< 0.1%
12.455106671
< 0.1%

WITHDRAW_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct248
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001048832009
Minimum0
Maximum0.1266666667
Zeros87477
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:17.233792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.007142857143
Maximum0.1266666667
Range0.1266666667
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.004570944472
Coefficient of variation (CV)4.358128312
Kurtosis86.24949972
Mean0.001048832009
Median Absolute Deviation (MAD)0
Skewness7.49616535
Sum101.0004248
Variance2.089353337 × 10-5
MonotonicityNot monotonic
2021-09-03T14:06:17.525290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
087477
90.8%
0.003333333333889
 
0.9%
0.006666666667592
 
0.6%
0.003448275862353
 
0.4%
0.01309
 
0.3%
0.01666666667308
 
0.3%
0.02275
 
0.3%
0.003571428571258
 
0.3%
0.007142857143239
 
0.2%
0.01428571429222
 
0.2%
Other values (238)5376
 
5.6%
ValueCountFrequency (%)
087477
90.8%
0.003333333333889
 
0.9%
0.003448275862353
 
0.4%
0.003571428571258
 
0.3%
0.003703703704218
 
0.2%
0.003846153846198
 
0.2%
0.004157
 
0.2%
0.004166666667138
 
0.1%
0.004347826087122
 
0.1%
0.004545454545125
 
0.1%
ValueCountFrequency (%)
0.12666666671
< 0.1%
0.12333333331
< 0.1%
0.12222222221
< 0.1%
0.11666666671
< 0.1%
0.11111111111
< 0.1%
0.096666666671
< 0.1%
0.09629629631
< 0.1%
0.091
< 0.1%
0.083333333331
< 0.1%
0.083333333331
< 0.1%

DEPOSIT_TRAILS
Real number (ℝ≥0)

ZEROS

Distinct8374
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1532384544
Minimum0
Maximum48.912
Zeros32090
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:17.805896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.025
Q30.1136363636
95-th percentile0.6423076923
Maximum48.912
Range48.912
Interquartile range (IQR)0.1136363636

Descriptive statistics

Standard deviation0.54000244
Coefficient of variation (CV)3.523935569
Kurtosis953.5366645
Mean0.1532384544
Median Absolute Deviation (MAD)0.025
Skewness19.51286685
Sum14756.55668
Variance0.2916026352
MonotonicityNot monotonic
2021-09-03T14:06:18.098311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032090
33.3%
0.5812
 
0.8%
0.25784
 
0.8%
0.05734
 
0.8%
0.05727
 
0.8%
0.025631
 
0.7%
0.1601
 
0.6%
0.1555
 
0.6%
0.2542
 
0.6%
0.1666666667539
 
0.6%
Other values (8364)58283
60.5%
ValueCountFrequency (%)
032090
33.3%
0.001666666667130
 
0.1%
0.00172413793167
 
0.1%
0.00178571428656
 
0.1%
0.00185185185249
 
0.1%
0.00192307692351
 
0.1%
0.0019615384621
 
< 0.1%
0.00230
 
< 0.1%
0.00208333333339
 
< 0.1%
0.00217391304337
 
< 0.1%
ValueCountFrequency (%)
48.9121
 
< 0.1%
24.903333331
 
< 0.1%
20.21
 
< 0.1%
18.881
 
< 0.1%
181
 
< 0.1%
16.93751
 
< 0.1%
16.6251
 
< 0.1%
16.051
 
< 0.1%
161
 
< 0.1%
153
< 0.1%

ENTRY_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28340
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.900724588
Minimum0
Maximum60.16666667
Zeros3127
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:18.375750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4541666667
median1
Q32.207407407
95-th percentile6.948534483
Maximum60.16666667
Range60.16666667
Interquartile range (IQR)1.753240741

Descriptive statistics

Standard deviation2.614192436
Coefficient of variation (CV)1.375366243
Kurtosis22.32103616
Mean1.900724588
Median Absolute Deviation (MAD)0.6833333333
Skewness3.650419797
Sum183035.9764
Variance6.834002092
MonotonicityNot monotonic
2021-09-03T14:06:18.647255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03127
 
3.2%
0.11533
 
1.6%
0.21335
 
1.4%
0.51129
 
1.2%
0.31085
 
1.1%
0.6950
 
1.0%
0.4923
 
1.0%
0.7646
 
0.7%
1598
 
0.6%
0.9598
 
0.6%
Other values (28330)84374
87.6%
ValueCountFrequency (%)
03127
3.2%
0.0034482758621
 
< 0.1%
0.0045454545452
 
< 0.1%
0.0058823529411
 
< 0.1%
0.0066666666671
 
< 0.1%
0.0083333333332
 
< 0.1%
0.0090909090912
 
< 0.1%
0.011
 
< 0.1%
0.010526315792
 
< 0.1%
0.011111111114
 
< 0.1%
ValueCountFrequency (%)
60.166666671
< 0.1%
47.641
< 0.1%
41.81
< 0.1%
40.561
< 0.1%
391
< 0.1%
38.451
< 0.1%
37.733333331
< 0.1%
36.244444441
< 0.1%
35.3751
< 0.1%
34.68751
< 0.1%

WINNINGS_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15141
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6208757527
Minimum0
Maximum19.1
Zeros9585
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:18.940900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1115384615
median0.316
Q30.7333333333
95-th percentile2.283333333
Maximum19.1
Range19.1
Interquartile range (IQR)0.6217948718

Descriptive statistics

Standard deviation0.9420872103
Coefficient of variation (CV)1.517352234
Kurtosis30.78910331
Mean0.6208757527
Median Absolute Deviation (MAD)0.2493333333
Skewness4.319497445
Sum59789.09324
Variance0.8875283119
MonotonicityNot monotonic
2021-09-03T14:06:19.255079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09585
 
10.0%
0.12447
 
2.5%
0.21704
 
1.8%
0.31470
 
1.5%
0.051348
 
1.4%
0.4952
 
1.0%
0.25870
 
0.9%
0.5850
 
0.9%
0.15767
 
0.8%
0.6673
 
0.7%
Other values (15131)75632
78.5%
ValueCountFrequency (%)
09585
10.0%
0.0033333333336
 
< 0.1%
0.0034482758625
 
< 0.1%
0.0035714285715
 
< 0.1%
0.0037037037047
 
< 0.1%
0.0038461538465
 
< 0.1%
0.0043
 
< 0.1%
0.0041666666679
 
< 0.1%
0.00434782608711
 
< 0.1%
0.0045454545456
 
< 0.1%
ValueCountFrequency (%)
19.11
< 0.1%
17.12
< 0.1%
15.21
< 0.1%
15.151
< 0.1%
14.511
< 0.1%
14.266666671
< 0.1%
14.151
< 0.1%
13.442857141
< 0.1%
13.203333331
< 0.1%
13.11
< 0.1%

PRACTICE_ENTRY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct6797
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2243730009
Minimum0
Maximum171.5043033
Zeros86672
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:19.552684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2560200128
Maximum171.5043033
Range171.5043033
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.445180498
Coefficient of variation (CV)10.89783748
Kurtosis930.8058975
Mean0.2243730009
Median Absolute Deviation (MAD)0
Skewness25.20468873
Sum21606.67124
Variance5.978907668
MonotonicityNot monotonic
2021-09-03T14:06:19.849350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086672
90.0%
0.25121
 
0.1%
0.0833333333385
 
0.1%
0.12585
 
0.1%
0.00833333333370
 
0.1%
0.062560
 
0.1%
0.559
 
0.1%
0.0166666666757
 
0.1%
0.0550
 
0.1%
0.00862068965546
 
< 0.1%
Other values (6787)8993
 
9.3%
ValueCountFrequency (%)
086672
90.0%
0.00016923076921
 
< 0.1%
0.000221
 
< 0.1%
0.00023571428571
 
< 0.1%
0.00029333333331
 
< 0.1%
0.00034482758621
 
< 0.1%
0.0003521
 
< 0.1%
0.00036666666671
 
< 0.1%
0.00037931034481
 
< 0.1%
0.00038461538461
 
< 0.1%
ValueCountFrequency (%)
171.50430331
< 0.1%
141.05833331
< 0.1%
112.64285711
< 0.1%
109.67409771
< 0.1%
105.6725551
< 0.1%
102.10888671
< 0.1%
101.92167331
< 0.1%
97.942589511
< 0.1%
92.682933331
< 0.1%
92.009407411
< 0.1%

PRACTICE_WINNINGS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5280
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1754707622
Minimum0
Maximum165.5908288
Zeros88830
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:20.147594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1252559216
Maximum165.5908288
Range165.5908288
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.099158808
Coefficient of variation (CV)11.96301186
Kurtosis1202.239488
Mean0.1754707622
Median Absolute Deviation (MAD)0
Skewness28.30539502
Sum16897.48346
Variance4.406467702
MonotonicityNot monotonic
2021-09-03T14:06:20.414940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088830
92.2%
0.2260
 
0.1%
0.4456
 
0.1%
0.0146666666752
 
0.1%
0.146666666751
 
0.1%
0.04449
 
0.1%
0.1147
 
< 0.1%
0.02239
 
< 0.1%
0.08838
 
< 0.1%
0.0733333333332
 
< 0.1%
Other values (5270)7044
 
7.3%
ValueCountFrequency (%)
088830
92.2%
0.00030344827592
 
< 0.1%
0.00031428571431
 
< 0.1%
0.00033846153853
 
< 0.1%
0.00038260869571
 
< 0.1%
0.000441
 
< 0.1%
0.00047142857141
 
< 0.1%
0.000551
 
< 0.1%
0.00058666666672
 
< 0.1%
0.0007042
 
< 0.1%
ValueCountFrequency (%)
165.59082881
< 0.1%
131.01733331
< 0.1%
111.82285711
< 0.1%
99.479049441
< 0.1%
96.928415671
< 0.1%
94.183348281
< 0.1%
90.621110931
< 0.1%
81.052951
< 0.1%
80.286457831
< 0.1%
79.335493331
< 0.1%

PRACTICE_ENTRY_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2484
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03108396993
Minimum0
Maximum12.61333333
Zeros86672
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:20.691570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07
Maximum12.61333333
Range12.61333333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2447859698
Coefficient of variation (CV)7.874990559
Kurtosis466.2289863
Mean0.03108396993
Median Absolute Deviation (MAD)0
Skewness17.77948968
Sum2993.324137
Variance0.05992017099
MonotonicityNot monotonic
2021-09-03T14:06:20.978851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086672
90.0%
0.1239
 
0.2%
0.05211
 
0.2%
0.03333333333193
 
0.2%
0.003333333333169
 
0.2%
0.01666666667148
 
0.2%
0.02143
 
0.1%
0.025129
 
0.1%
0.006666666667122
 
0.1%
0.2120
 
0.1%
Other values (2474)8152
 
8.5%
ValueCountFrequency (%)
086672
90.0%
0.003333333333169
 
0.2%
0.00344827586296
 
0.1%
0.00357142857171
 
0.1%
0.00370370370458
 
0.1%
0.00384615384659
 
0.1%
0.00457
 
0.1%
0.00416666666747
 
< 0.1%
0.00434782608746
 
< 0.1%
0.00454545454543
 
< 0.1%
ValueCountFrequency (%)
12.613333331
< 0.1%
11.5721
< 0.1%
9.6666666671
< 0.1%
8.7751
< 0.1%
8.7333333331
< 0.1%
8.61
< 0.1%
8.3666666671
< 0.1%
8.3172413791
< 0.1%
8.1862068971
< 0.1%
7.6066666671
< 0.1%

PRACTICE_WINNINGS_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1744
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01261088529
Minimum0
Maximum6.568
Zeros88830
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:21.256109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.024
Maximum6.568
Range6.568
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1059958064
Coefficient of variation (CV)8.405104321
Kurtosis654.6647885
Mean0.01261088529
Median Absolute Deviation (MAD)0
Skewness20.13494762
Sum1214.403031
Variance0.01123511098
MonotonicityNot monotonic
2021-09-03T14:06:21.532545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088830
92.2%
0.03333333333192
 
0.2%
0.1185
 
0.2%
0.05180
 
0.2%
0.003333333333151
 
0.2%
0.02142
 
0.1%
0.01666666667132
 
0.1%
0.006666666667121
 
0.1%
0.025117
 
0.1%
0.06666666667105
 
0.1%
Other values (1734)6143
 
6.4%
ValueCountFrequency (%)
088830
92.2%
0.003333333333151
 
0.2%
0.00344827586292
 
0.1%
0.00357142857160
 
0.1%
0.00370370370446
 
< 0.1%
0.00384615384651
 
0.1%
0.00442
 
< 0.1%
0.00416666666743
 
< 0.1%
0.00434782608740
 
< 0.1%
0.00454545454533
 
< 0.1%
ValueCountFrequency (%)
6.5681
< 0.1%
6.4266666671
< 0.1%
4.9251
< 0.1%
4.241
< 0.1%
4.0259259261
< 0.1%
3.8518518521
< 0.1%
3.7370370371
< 0.1%
3.5533333331
< 0.1%
3.3666666671
< 0.1%
3.0666666671
< 0.1%

Y1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct83790
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.364037072
Minimum2.604128227
Maximum834.0631553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:21.825606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.604128227
5-th percentile3.961575323
Q13.984063745
median4.018940073
Q34.332848275
95-th percentile8.832545808
Maximum834.0631553
Range831.4590271
Interquartile range (IQR)0.3487845303

Descriptive statistics

Standard deviation8.439211114
Coefficient of variation (CV)1.573294703
Kurtosis1290.697179
Mean5.364037072
Median Absolute Deviation (MAD)0.04505951195
Skewness23.77190203
Sum516546.042
Variance71.22028423
MonotonicityNot monotonic
2021-09-03T14:06:22.107646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9840637452033
 
2.1%
3.985657371588
 
0.6%
3.979083665244
 
0.3%
3.977290837230
 
0.2%
3.974103586227
 
0.2%
3.990039841166
 
0.2%
3.985557769130
 
0.1%
3.987051793117
 
0.1%
3.977091633107
 
0.1%
3.98725099694
 
0.1%
Other values (83780)92362
95.9%
ValueCountFrequency (%)
2.6041282271
< 0.1%
3.4187000341
< 0.1%
3.4955115551
< 0.1%
3.5318254311
< 0.1%
3.5332299491
< 0.1%
3.5490458181
< 0.1%
3.5669562431
< 0.1%
3.6135195231
< 0.1%
3.6148406231
< 0.1%
3.616356211
< 0.1%
ValueCountFrequency (%)
834.06315531
< 0.1%
398.32754381
< 0.1%
360.69375331
< 0.1%
310.4162731
< 0.1%
306.77366961
< 0.1%
299.8894361
< 0.1%
295.43246781
< 0.1%
269.59697211
< 0.1%
265.5578061
< 0.1%
263.79400991
< 0.1%

Y2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1343
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.9806949
Minimum0
Maximum1000
Zeros2618
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2021-09-03T14:06:22.392733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.739726027
Q18.219178082
median27.39726027
Q3131.5068493
95-th percentile558.9041096
Maximum1000
Range1000
Interquartile range (IQR)123.2876712

Descriptive statistics

Standard deviation185.2854953
Coefficient of variation (CV)1.611448734
Kurtosis5.040546728
Mean114.9806949
Median Absolute Deviation (MAD)24.65753425
Skewness2.297280738
Sum11072410.96
Variance34330.71475
MonotonicityNot monotonic
2021-09-03T14:06:22.681009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.4794520559313
 
9.7%
2.7397260278626
 
9.0%
8.2191780826162
 
6.4%
10.958904115227
 
5.4%
02618
 
2.7%
16.438356162414
 
2.5%
21.917808222202
 
2.3%
13.698630141930
 
2.0%
19.178082191783
 
1.9%
24.657534251504
 
1.6%
Other values (1333)54519
56.6%
ValueCountFrequency (%)
02618
 
2.7%
2.7397260278626
9.0%
2.7397260274
 
< 0.1%
2.7397260276
 
< 0.1%
2.7397260274
 
< 0.1%
5.4794520559313
9.7%
5.47945205515
 
< 0.1%
5.4794520555
 
< 0.1%
5.4794520559
 
< 0.1%
8.21917808262
 
0.1%
ValueCountFrequency (%)
100023
< 0.1%
997.2602746
 
< 0.1%
994.52054799
 
< 0.1%
991.78082196
 
< 0.1%
989.04109599
 
< 0.1%
986.301369911
< 0.1%
983.56164386
 
< 0.1%
980.82191782
 
< 0.1%
980.82191788
 
< 0.1%
978.08219189
 
< 0.1%

Interactions

2021-09-03T14:04:29.676176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:29.910299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:30.118494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:30.323673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:30.524593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:30.730022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:30.941151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:31.150666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:31.364972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:31.580339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:31.789829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:32.005342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:32.221165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:32.431890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:32.647567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:32.876450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:33.100844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:33.323966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:33.536743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:33.763895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:33.973426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:34.189031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:34.402306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:34.892992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:35.123682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:04:35.335731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-09-03T14:04:35.728161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-09-03T14:06:02.827434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:03.024019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:03.271787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:03.481285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:03.692786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:03.897402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:04.110837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:04.313976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:04.527230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:04.741956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:04.960823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:06.063635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:06.270907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:06.469471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:06.671330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:06.887323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-03T14:06:07.084734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-03T14:06:22.997899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-03T14:06:23.442699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-03T14:06:23.870048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-03T14:06:24.312737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-03T14:06:24.684183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-03T14:06:07.534556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-03T14:06:08.788999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

SEQUENCE_NOSTATUS_CHECKCATEGORY_1CATEGORY_2ACTIVE_YNENTRYREVENUEWINNINGS_1WINNINGS_2DISCOUNTDEPOSITDEPOSIT_NUMBERDEPOSIT_2WITHDRAWWITHDRAW_NUMBERDEPOSIT_TRAILSENTRY_NUMBERWINNINGS_NUMBERPRACTICE_ENTRYPRACTICE_WINNINGSPRACTICE_ENTRY_NUMBERPRACTICE_WINNINGS_NUMBERY1Y2
04.00MB1.0000000.1122360.0086820.0529290.0000.0000000.0357140.0142860.0357140.0000000.0000000.0000000.9285710.4428570.00.00.00.04.449287101.369863
110.01MI0.94736830.2857423.73345525.1206180.0001.16315810.2631580.0526320.5263161.3578950.0105260.6842110.7000000.2473680.00.00.00.07.182779115.068493
29.50MD1.0000000.0837200.0075400.0528280.0000.0333330.0138890.0055560.0138890.0000000.0000000.0277781.3111110.3055560.00.00.00.03.97737124.657534
33.00ME1.0000000.7770000.1222200.0170000.0000.2400000.7000000.0400000.2000000.0000000.0000000.5400000.9000000.0400000.00.00.00.03.98812210.958904
41.00MI1.0000000.5000000.0800000.0000000.0000.0000000.5000000.1000000.5000000.0000000.0000000.0000000.2000000.0000000.00.00.00.04.12887546.575342
51.00MI1.0000000.0000000.0000000.0000000.0000.0000000.5000000.1000000.5000000.0000000.0000000.0000000.0000000.0000000.00.00.00.03.9840640.000000
615.01MI1.0000007.1822261.0691785.3821780.0000.6434482.1896550.0965520.0344830.1724140.0068970.0620692.1551720.7551720.00.00.00.08.590365597.260274
713.01MI1.00000032.4882772.85974422.7036760.0001.6414886.0000000.0360000.0400000.0000000.0000000.9120004.0200001.0600000.00.00.00.06.42367863.013699
81.00MD1.0000000.5500000.0806250.3400000.0000.0000000.2500000.1000000.2500000.0000000.0000000.0000002.2000000.8000000.00.00.00.03.9872762.739726
95.50MI1.0000001.6078520.2543971.1569550.0150.2040000.4000000.0700000.1000000.0000000.0000000.0500001.6800000.7000000.00.00.00.04.345791205.479452

Last rows

SEQUENCE_NOSTATUS_CHECKCATEGORY_1CATEGORY_2ACTIVE_YNENTRYREVENUEWINNINGS_1WINNINGS_2DISCOUNTDEPOSITDEPOSIT_NUMBERDEPOSIT_2WITHDRAWWITHDRAW_NUMBERDEPOSIT_TRAILSENTRY_NUMBERWINNINGS_NUMBERPRACTICE_ENTRYPRACTICE_WINNINGSPRACTICE_ENTRY_NUMBERPRACTICE_WINNINGS_NUMBERY1Y2
962885.50BB1.057.9048365.14584645.6665850.0000002.0000005.6700000.1500000.5000000.0000000.0000002.85000020.2700003.9300000.0000000.00.0000000.05.83499927.397260
9628915.51BD1.00.8106780.1206170.6033900.0208330.4333330.2166670.0100000.1666670.0466670.0066670.1566670.7033330.1533330.0000000.00.0000000.04.158875131.506849
9629014.51KI1.06.4643610.9757983.8833520.0485161.0285712.4482140.1357140.1785710.0500000.0035710.0410711.5678570.4678570.0000000.00.0000000.023.593541605.479452
9629112.51KI1.00.7314580.0768930.2687210.2669500.3400000.4166670.0166670.1041670.0000000.0000000.0000000.1208330.0375000.0000000.00.0000000.04.459694189.041096
962923.00BI1.00.1000000.0150000.0000000.0000000.0000000.1000000.0200000.1000000.0000000.0000000.0000000.0400000.0000000.0000000.00.0000000.03.9850602.739726
962932.50BB1.012.6894071.6555397.1968750.0000009.3000002.8750000.1250001.8750000.0000000.0000000.0000002.8750000.7250000.0000000.00.0000000.03.87618210.958904
962945.00CI1.038.2640034.53499331.7024440.0000001.0444444.0555560.1222220.1111110.0000000.0000001.05555611.8111113.1888890.0333330.00.0111110.05.51653124.657534
9629515.50BI1.00.9981210.1338200.6508470.2808230.0400000.3250000.0533330.0083330.0000000.0000000.0033331.0966670.2433330.0000000.00.0000000.05.268355449.315068
9629615.50BD1.00.4408330.0662920.4392100.0141670.0133330.0083330.0033330.0083330.0000000.0000000.0000000.9533330.4366670.0000000.00.0000000.04.086504156.164384
962972.50BD1.00.1854180.0224010.0552750.0000000.0000000.1250000.0250000.1250000.0000000.0000000.0000000.7500000.0750000.0000000.00.0000000.03.95911243.835616

Duplicate rows

Most frequently occurring

SEQUENCE_NOSTATUS_CHECKCATEGORY_1CATEGORY_2ACTIVE_YNENTRYREVENUEWINNINGS_1WINNINGS_2DISCOUNTDEPOSITDEPOSIT_NUMBERDEPOSIT_2WITHDRAWWITHDRAW_NUMBERDEPOSIT_TRAILSENTRY_NUMBERWINNINGS_NUMBERPRACTICE_ENTRYPRACTICE_WINNINGSPRACTICE_ENTRY_NUMBERPRACTICE_WINNINGS_NUMBERY1Y2# duplicates
1331.00BI1.00.250.040.00.00.00.250.10.250.00.00.00.10.00.00.00.00.03.9856572.739726101
951.00BI1.00.000.000.00.00.00.250.10.250.00.00.00.00.00.00.00.00.03.9840640.000000100
1021.00BI1.00.000.000.00.00.00.500.10.500.00.00.00.00.00.00.00.00.03.9840640.00000062
2771.00KI1.00.000.000.00.00.00.250.10.250.00.00.00.00.00.00.00.00.03.9840640.00000057
1701.00BI1.01.000.150.00.00.01.000.11.000.00.00.00.10.00.00.00.00.03.9900402.73972653
1351.00BI1.00.250.040.00.00.00.250.10.250.00.00.00.20.00.00.00.00.03.9856572.73972649
1081.00BI1.00.000.000.00.00.01.000.11.000.00.00.00.00.00.00.00.00.03.9840640.00000047
591.00BD1.00.000.000.00.00.00.250.10.250.00.00.00.00.00.00.00.00.03.9840640.00000040
361.00BB1.00.000.000.00.00.00.250.10.250.00.00.00.00.00.00.00.00.03.9840640.00000031
2831.00KI1.00.000.000.00.00.00.500.10.500.00.00.00.00.00.00.00.00.03.9840640.00000029